The pattern recognition approach to self-tuning is unique. It uses direct-performance feedback of a monitored process variable to determine the required action for optimizing the process. More specifically, a pattern-recognition, self-tuning controller continuously monitors and automatically evaluates a closed-loop response pattern of a measured process variable to naturally occurring, unmeasured upsets caused by a change in set point or load. After each upset, closed-loop error response characteristics such as overshoot and decay are identified and compared, with desired characteristics. Differences between the desired characteristics and the measured characteristics are then used to automatically generate new tuning values for adjusting the controller according to the requirements of the process in order to achieve an error response target shape. The target shape is chosen to approximately minimize integrated absolute error.
The first successful adaptive process control system to automatically identify and evaluate process response characteristics of the closed-loop response pattern was invented by Edgar H. Bristol, II and is disclosed in U.S. Pat. No. 3,798,426. According to Bristol's method, the adaptation system is triggered by detecting the closed-loop response pattern resulting from an upset having a magnitude which exceeds a preselected noise band. Once triggered, dead and rise times are identified by measuring the time required for the response pattern to reach predetermined percentages of the extremum value of the upset during its first half cycle. The dead time is assumed to bear a significant relationship to any process dead time which is most apparent early in the measured variable response, and rise time is assumed to bear a significant relationship to process closed-loop natural period.
The measured rise time is scaled to establish evaluation intervals for the adaptation process. Scaling constants, used to determine these intervals, are selected so that the first half cycle in a resonant response is developed during one evaluation interval and the first full cycle is fully developed during another. The response pattern is evaluated by calculating the integrated difference between the measured process control error, normalized by the magnitude of the first error response peak, and a target value for each of the evaluation intervals. The integrated differences are used to adapt the operating parameters of the controller to improve control action during the next process upset.
For an effective adaptive process control system, several critical parameters must be specified by an operator. For example, the proper selection of scaling constants is critical for defining the appropriate evaluation intervals. Universal scaling constants cannot be used for different types of processes. This control system also requires the operator to select an appropriate target value for each evaluation interval. These target values are typically derived from the operator's experience with a given process.
An improved pattern-recognition, self-tuning controller was developed by Thomas W. Kraus. According to Kraus' method, the adaptive process is initiated when the error exceeds a nominal noise threshold. Once initiated, the closed-loop response pattern is monitored to detect the first three successive extremum values or "peaks" and their times of occurrence relative to the first peak.
Since it is common to find an overdamped control loop response without three peaks, Kraus' adaptive process automatically recognizes the response as overdamped if, after a pre-specified wait period after verifying the first peak, the second peak is not found. If a second peak is found, the third peak is sought for a time period proportional to the time between the first and second peaks. If the second or third peak is not detected, the search for peaks is terminated and "pseudo" peak values are assigned.
Characteristics of the closed-loop response pattern, such as overshoot, damping, and period, are then calculated using the measured extremum values of the response pattern. Differences between these measured characteristics and desired characteristics are then used to calculate new control operating parameters to optimize the control action. This method is described in greater detail in U.S. Pat. No. 4,602,326, issued to Thomas Kraus, and entitled, "Pattern-recognition, Self-tuning Controller." As described in both patents to Bristol and Kraus, which are incorporated by reference, the system tunes the controller for the last disturbance. This can result in non-optimum tuning of the controller for the next disturbance, if the process is nonlinear.
Kraus' system also has critical parameters which must be specified by an operator. For example, choosing the pre-specified wait period in Kraus' system is critical, particularly when the process operates over a wide range of conditions. This wait period is critical because it also establishes a wait time before the peak search is activated. If it is set improperly, the system may not operate efficiently. The system also has a tendency to tighten the tuning of the process when the second and third peaks are lost in the noise band. After several disturbances, the response can become excessively oscillatory, causing the system to overcorrect the process which results in overdamping the response. Further, thresholds for the noise band are either user-selected or determined during a pretuning operation for an open loop condition. It does not adaptively respond to condition changes in a closed-loop process.